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Brain Sci. 2022, 12, 1312. https://doi.org/10.3390/brainsci12101312 www.mdpi.com/journal/brainsci
Article
Symptoms of Attention Deficit/Hyperactivity Disorder Are
Associated with Sub-Optimal and Inconsistent Temporal
Decision Making
Ortal Gabrieli-Seri
1
, Eyal Ert
2
and Yehuda Pollak
1,
*
1
The Seymour Fox School of Education, The Hebrew University of Jerusalem, Jerusalem 9190501, Israel
2
Department of Environmental Economics and Management, The Hebrew University of Jerusalem,
Rehovot 7610001, Israel
* Correspondence: yehuda.pollak@mail.huji.ac.il
Abstract: The link between Attention-Deficit/Hyperactivity Disorder (ADHD) and steeper delay
discounting has been established and incorporated into theories of ADHD. This study examines a
novel interpretation according to which ADHD is linked to sub-optimal temporal decision-making
and suggests inconsistency as a potential underlying mechanism. In two experiments, MTurk
workers completed a self-report questionnaire on symptoms of ADHD and a temporal decision
making task consisting of choices between smaller–immediate and larger–delayed options. The
delayed option was better in some items, whereas the immediate option was better in others. The
rate of choices of the delayed option and the consistency of choices were measured. The results of
both studies show that high symptoms of ADHD were linked to fewer choices of the delayed op-
tion when it was better, but also to more choices of the delayed option when it was not better. In
addition, ADHD was linked to higher inconsistency in both conditions. The findings suggest that
ADHD is linked to sub-optimal temporal decision-making rather than steeper delay discounting,
and provide further support to the phenomenon of inconsistency in ADHD.
Keywords: ADHD; decision-making; delay discounting; suboptimal; consistency
1. Introduction
Attention-Deficit/Hyperactivity Disorder (ADHD) is a neuro-developmental condi-
tion defined by persistent symptoms of inattention and/or hyperactivity–impulsivity
interfering with functioning or development [1]. ADHD is highly prevalent (5.9% in
youth and 2.5% in adults) and has been proven to reduce various dimensions of quality
of life [2–5]. A significant deficit that has been related to ADHD is impaired time-related
(temporal) decision-making [6–8], which was thought to reflect impulsivity, a core
symptom of ADHD [9,10].
Time-related decisions are traditionally characterized by the term delay discount-
ing, meaning that a future payoff has a lower utility than an equivalent payoff in the
present. The research on this topic is vast; however, it is mostly agreed that delay dis-
counting is subjective and can be determined experimentally (for a review, see Frederick
et al. [11] ). For example,
Mazur [12] presented a discounting rate parameter k (in the
function 𝑉=
, where V is the immediate reward, A is the delayed reward, and D is
the length of the delay) that is computed from the participant’s pattern of choice in a
monetary choice questionnaire [13].
Research on temporal decisions often includes a temporal discounting task: the
participant is asked to choose between two monetary rewards, a small and immediate or
a larger but delayed [9,14]. As mentioned above, regarding ADHD, the evidence about
the link between the disorder and delay discounting supposedly points to a bias towards
Citation: Gabrieli-Seri, O.; Ert, E.;
Pollak, Y. Symptoms of Attention
Deficit/Hyperactivity Disorder Are
Associated with Sub-Optimal and
Inconsistent Temporal Decision
Making. Brain Sci. 2022, 12, 1312.
https://doi.org/10.3390/
brainsci12101312
Academic Editor: Caterina Cinel
Received: 31 August 2022
Accepted: 23 September 2022
Published: 28 September 2022
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(https://creativecommons.org/license
s/by/4.0/).
Brain Sci. 2022, 12, 1312 2 of 13
steeper delay discounting [6], i.e., an even lower utility assigned to future payoff than
that in controls. Specifically, it has been found that people with ADHD tend to choose the
small immediate reward over the larger delayed reward more often than controls [6]. The
findings have often been interpreted as reflecting steeper delay discounting modeled by a
larger discount parameter among people with ADHD.
While the evidence for immediacy bias seems clear, the studies documenting this
tendency have focused on cases where the delayed reward option is better than the im-
mediate one. This focus is well justified and characterizes many real-life temporal di-
lemmas. However, there might be situations in which it is better to choose the immediate
payoff, or at least most people would agree. For an obvious example, consider many
banks saving accounts that currently offer incredibly low interest rates. Most people
would agree that investing USD 1000 in an account with a yearly interest of 0.01% is not
worthwhile or profitable. The calculated k parameter for this problem (i.e., USD 1,000
now or USD 1,010 after one year) is around 0.00003, which is very low, so an individual’s
k value needs to be at this level or lower for them to accept such an offer. Despite such
examples, studies of time preferences of people with ADHD have implicitly assumed
that the delayed reward is the better choice. In other words, optimality and delay have
been confounded, which raises the question of whether this confounding might have led
to overlooking alternative explanations for the immediacy tendency among people with
ADHD.
An alternative explanation to the temporal decision-making deficit in ADHD is a
sub-optimal valuation of delay. This novel interpretation states that ADHD might be re-
lated to sub-optimal choices regardless of immediacy. Specifically, when the delayed
option is better, people with ADHD might indeed lean towards the immediate option.
Still, when the immediate option offers the better choice, people with ADHD might lean
towards the less attractive delayed option. This latter tendency, if it exists, contradicts the
previously mentioned conclusions regarding steeper delay discounting. Specifically, if
ADHD is linked with immediacy preference, people with ADHD should have an ad-
vantage in immediate-is-better situations. The first goal of this research is to compare
these distinct interpretations.
The current study’s second goal is to explore choice inconsistency as a possible ex-
planation for temporal decision-making in ADHD. The consistency of preferences is a
core element for making optimal decisions [15], and inconsistency in decision-making
might lead to sub-optimal decisions, [16]. ADHD has been linked to inconsistency in
different aspects, such as reaction times [17–19], time estimation [20], working memory
[21], driving behavior [22], and self-reports of delinquency and impulsivity [23,24]. In the
context of decision-making, ADHD has been related to inconsistency in decisions under
risk, and was suggested as a possible explanation for sub-optimal decision-making [25].
The current study tests whether ADHD is linked to higher inconsistency in temporal de-
cision-making.
The current paper examined the sub-optimal valuation and inconsistency hypothe-
ses using two studies. In Experiment 1, we used a between-subject design to explore the
hypotheses that people with a high level of symptoms of ADHD will show sub-optimal
decision-making in both “better–delayed” and “better–immediate” conditions. Experi-
ment 2 used a within-subject design, where the same participants chose between two
options under both better–delayed and better–immediate conditions.
2. Materials and Methods
Experiment 1
Participants
The sample consisted of 200 Amazon Mechanical Turk workers (age range = 18–74,
49.5% female) with a “Master” qualification (workers with high credibility). The partici-
pants received a compensation of USD 0.8 for their participation. After giving their in-
formed consent, participants were randomly assigned to the study task conditions. All
Brain Sci. 2022, 12, 1312 3 of 13
studies reported here were approved by the Ethics Committee of the Seymour Fox School
of Education, The Hebrew University of Jerusalem.
Materials
A revised version of the monetary-choice questionnaire (MCQ) based on Kirby et
al.’s [13] scale assessed the delay discounting level. Participants were presented with a
fixed set of choices between immediate rewards and larger delayed rewards, e.g.,
“Would you prefer $30 today or $35 in 80 days?” and marked their preferred option. The
scales included 27 questions (See Table A1). The order of questions was contrived so that
the trial order would not correlate with the immediate or delayed amounts, their ratio,
their difference, the delay to the larger reward, or the discount rate (k) corresponding to
indifference between the two rewards. The original scale was split, and items were added
to create two scales of 27 items, one with low k values (0.00016–0.0025) and one with high
k values (0.045–0.35), to create the two conditions “better–immediate” and “better–
delayed”. The items were presented in a fixed order to ensure that the k values were
mixed properly (following Kirby et al. [13]).
The Adult ADHD Self Report Scale (ASRS-V1.1) [26], adapted for computer
presentation, was completed for continuous scaling of ADHD symptoms. A dimensional
model of ADHD was adopted for this study, as taxometric and genetic evidence has
shown that a dimensional conceptualization of ADHD has merits for research purposes
[27]. The ASRS-V1.1 contains 18 items corresponding to the DSM-IV diagnostic criteria
for ADHD. The symptoms are rated on a Likert 5-point scale by frequency of occurrence
ranging from “never” to “very often”. The questionnaire has high validity and reliability
(internal consistency, α = 0.88) in assessing ADHD in adults. Its reported sensitivity is
68.4% and its specificity is 99.6% [28]. The ASRS score was calculated by averaging the
scores of all 18 items (1 = low, 5 = high).
Attention check questions. To verify that the participants attended to the content of
the task, two questions were added: in the middle of the MCQ task, the participants were
asked to choose the immediate option (“If you read this, please choose $33 today”), and
in the middle of the ASRS questionnaire, to select “often” (“If you read this, please choose
“often”).
In addition, participants reported demographic information (age, gender, income
level, and years of education) and ADHD-related information (ADHD medication on the
day of the experiment and a history of ADHD diagnosis).
Data analysis
In total, 2 participants who failed the attention check and 23 who completed the
whole study in less than four minutes were excluded from further analysis (all reported
effects remained when the analysis included all 198 participants, except for the immedi-
ate condition, in which the effect for the proportion of delayed options did not reach
significance ). The latter criterion was applied to improve data quality, which was found
to be poorer when “speeders” were included in online samples [29]. The final sample
consisted of 175 participants (see Table 1 for descriptive statistics by optimality condi-
tion).
Table 1. Demographic and clinical characteristics by optimality condition groups.
Immediate is Better (n = 82) Delayed is Better (n = 93) Group Comparison
Age range Mode (%) 35–44 (37.8%) 35–44 (33.3%) t(169) = 1.71 (p = 0.088)
Gender ratio 37 females (45.1%) 49 females (52.7%) χ2(1) = 0.72 (p = 0.397)
Annual Income category
Mode (%) USD 10,000-24,999 (28%) USD 10,000-24,999 (22.6%) χ2(7) = 9.21 (p = 0.243)
Education level Mode (%) Undergraduate (42.7%) Undergraduate (33.3%) χ2(4) = 4.77 (p = 0.314)
Sel
f
-reported history of
ADHD diagnosis Three diagnosed (3.7%) Six diagnosed (6.5%) χ2(1) = 0.24 (p = 0.504)
Brain Sci. 2022, 12, 1312 4 of 13
The consistency score was calculated by ordering the items and computing the per-
centage of choices most consistent with an indifference point at one of the different k
values of the questionnaire [13]. For example, suppose a participant chose the immediate
option in the first 24 items (ordered by discounting rate) and switched to the delayed
option for the last 3 items with the higher rate. In that case, their choices were 100% con-
sistent with the k value of the 24th item. However, suppose a participant mainly chose the
immediate option and only chose the delayed option at the 6th, 8th, 10th, 25th, and 26th
items. In that case, their assigned k value will be identical to the previous participant, but
with a consistency score of 85%. The discounting rate was operationalized as the percent
of times a participant chose the delayed reward. The variables age, gender, socioeco-
nomic position, and education were controlled. Regression analyses were performed to
examine the relationship between ADHD, delay discounting, and optimality. Consider-
ing previous findings of the relationship between delay discounting and psychiatric
variables [30], we examined a quadratic model in addition to the linear one. Specifically,
when it is better to choose the delayed reward, we expected a higher rate of choosing the
immediate option, with high levels of ADHD symptoms. In the better–immediate condi-
tion, we expected the opposite: higher delay scores for high symptoms of ADHD.
Experiment 2
Experiment 2 was conducted to evaluate the robustness of Experiment 1′s findings
using a within-subject design. The same participants chose between two options under
both better–delayed and better–immediate conditions, enabling us to examine whether
the same participants made sub-optimal choices under both conditions.
Participants
One hundred participants (age range =18–74 (Mdn = 25–34); 31% female) were re-
cruited the same way as described in Experiment 1. The participants were instructed to
choose the option they preferred in each of the 27 choice problems presented. The data of
14 participants were removed from the analysis due to response times shorter than four
minutes.
Materials
The experiment used the same tasks and materials as in Experiment 1, with the fol-
lowing adaptations of the main task for a within-subjects design:
The monetary-choice questionnaire (MCQ) based on Kirby et al.’s [13] scale was re-
vised to include more values of k, and the same scale of 27 items (see Table A2) was pre-
sented to all participants. The scale included 18 items corresponding to Experiment 1’s
“better–delayed” and “better–immediate” conditions (9 each); the remaining 9 items had
“medium” k values, intended to mask the large difference between the low and high
items.
Data analysis. The questionnaires and the task were coded the same way as in Ex-
periment 1. A repeated-measures ANOVA was used with low, medium, and high k levels
of the 27 choices as the within-subjects variable. The ASRS score and ASRS squared score
were continuous covariates. A second analysis used consistency score as the with-
in-subjects variable. Demographics and clinical information of the participants are pre-
sented in Table 2.
Table 2. Demographic and clinical characteristics, Experiment 2.
N = 91
Age Mean (SD) 42.92 (11.32)
Gender ratio 37 females (41%)
Annual income category Mode (%) USD 25,000-49,999 (26%)
Education level Mode (%) Undergraduate (40%)
Self-reported history of ADHD diagnosis Five diagnosed (5.5%)
Brain Sci. 2022, 12, 1312 5 of 13
3. Results
3.1. Experiment 1
Regression analyses were used to examine linear and curvilinear relations between
the level of symptoms of ADHD and sub-optimal temporal decision-making. Separate
analyses were performed for the two conditions (“better–delayed” and “better–
immediate”). In the better–delayed condition, the linear model was significant (F (1, 91) =
30.10, p <0.001, R
2
= 0.25), explaining 25% of the variance in delayed choices. The results
reveal that people with higher ASRS scores chose more immediate rewards than those
with lower scores (β = −0.50, p < 0.001, 95% CI (−0.68, −0.32)). The quadratic model had
better fit (F (2, 90) = 19.60, p < 0.001, R
2
= 0.30), showing that the level of delayed choices
increased and then (more steeply) dropped with the increase in ASRS scores (β = −1.41, p
= 0.009, 95% CI (−2.46, −0.36)). In the better–immediate condition, the linear model was
insignificant (F (1, 80) = 0.57, p = 0.452, R
2
= 0.08), while the quadratic model was signifi-
cant (F (2, 79) = 4.32, p = 0.017, R
2
= 0.10), explaining 10% of the variance in delayed
choices. It showed that ASRS significantly predicted delayed choices in an opposite pat-
tern from the “better–delayed” condition (β = 1.68, p = 0.006, 95% CI (0.50, 2.86)). That is,
the pattern of the quadratic model shows that the level of delayed choices decreased and
then increased with the rise in ASRS score. The quadratic curves in both conditions are
depicted in Figure 1a.
Figure. 1. (a) Regressions (quadratic term) of the relationship between the level of ADHD
symptoms (ASRS (ADHD Self Report Scale) mean score) and the proportion of choices to
wait for the two conditions: delay is better (solid line), and immediate is better (dashed
line; R²= .30; R²= .10, respectively). (b) Regressions (quadratic term) of the relationship
between the level of ADHD symptoms (ASRS mean score) and the proportion of con-
sistent choices for the two conditions: delay is better (solid line), and immediate is better
(dashed line; R²= .44; R²= .43, respectively).
We next examined whether the level of ADHD affects choice consistency. Specifi-
cally, we regressed the rate of consistent choices on the ASRS score under each of the two
aforementioned regression models (see Figure 1b for the quadratic curves). In the better–
delayed condition, the linear model was significant (F (1, 91) = 51.59, p < 0.001), explaining
36% of the change in delayed choices (R
2
= 0.36). The quadratic model improved the
prediction to 44% (F (2, 90) = 35.38, p < 0.001, R
2
= 0.44). In the better–immediate condition,
the linear model was also significant (F (1, 80) = 39.11, p < 0.001), explaining 33% of the
change in delayed choices (R
2
= 0.33). The quadratic model improved the prediction to
43% (F (2, 79) = 29.74, p < 0.001, R
2
= 0.43). In both conditions, a higher ASRS score signif-
icantly predicted less consistency in choices (β = −0.60, p < 0.001, 95% CI (−0.77, −0.43); β =
−0.57, p < 0.001, 95% CI (−0.75, −0.39); respectively). According to the quadratic model, the
level of consistency somewhat increased and then (more steeply) dropped with the rise in
Brain Sci. 2022, 12, 1312 6 of 13
ASRS scores, in both conditions (β = −1.68, p = 0.001, 95% CI (−2.63, −0.74); β = −1.76, p <
0.001, 95% CI (−2.70, −0.83); respectively).
The results suggest that participants who reported a high level of ADHD symptoms
showed lower consistency than participants with a low level of ADHD symptoms, in-
dependently of the optimality conditions. Participants who reported a high level of
ADHD symptoms showed lower consistency than participants with a low level of ADHD
symptoms, independently of the optimality conditions.
When the delayed option was better, participants with a high level of ADHD chose
the immediate option more often than participants with a low level of ADHD, replicating
the pattern of delay discounting as documented by previous research. Yet when the
immediate option was better, participants with high levels of ADHD symptoms chose the
delayed option more often, in contrast to the steeper delay discounting hypothesis. Fur-
thermore, choices made by the high ADHD participants were less consistent under both
conditions. The results suggest that people with a high level of ADHD symptoms do not
always exhibit a stronger preference for immediate rewards, as assumed by the literature,
but might instead exhibit less optimal temporal decision-making than controls.
3.2. Experiment 2
We conducted a repeated-measures ANOVA with the three levels of k (low, medi-
um, high) as the dependent variables, and the ASRS score and ASRS squared score as
continuous covariates, to examine the hypothesis of sub-optimal decision-making in re-
lation to high levels of symptoms of ADHD. The analysis yielded a significant interaction
effect between both covariates and delayed choices (ASRS: (F (2, 82) = 6.43, p = 0.003,
η
= 0.14, 95% CI (0.20, 0.26)); ASRS squared: (F (2, 82) = 10.11, p < 0.001, η
= .20, 95% CI
(0.05, 0.330)). The main effect was not found for delayed choices (F (2, 82) = 0.67, p = 0.52).
To explore the delay × ASRS interaction, we performed separate post hoc regression
tests for the three k levels (Figure 2a). When the k level was high, i.e., the delayed reward
was better, the linear model was significant (F (1, 84) = 27.47, p < 0.001, R
2
= 0.25), ex-
plaining 25% of the variance in delayed choices. ASRS significantly predicted less de-
layed choices (β = −0.50, p < 0.001, 95% CI (−0.70, −0.31)). The quadratic model improved
the prediction to 30% (F (2, 83) = 17.97, p < 0.001, R
2
= 0.30), which implies a rise in choices
of the delayed reward that dropped with the rise in ASRS score (β = −1.26, p = 0.012, 95%
CI (−2.30, −0.29)). When the k level was low, and it was better to choose the immediate
reward, the linear model was insignificant (F (1, 84) = 0.31, p = 0.580). However, the
quadratic model was significant and explained 10% of the variance (F (2, 83) = 4.78, p =
0.011, R
2
= 0.10), according to which there was a decrease in the delay options chosen and
then a rise compliant with the rise in ASRS score (β = 1.68, p = 0.003, 95% CI (0.09, 0.42)).
For the middle items, the results were similar to those of the high-level delay items.
Brain Sci. 2022, 12, 1312 7 of 13
Figure 2. (a) Regressions (quadratic term) of the relationship between the level of ADHD
symptoms (ASRS mean score) and the proportion of choices to wait for low (solid line),
medium (dotted line), and high (dashed line) levels. i.e., low= better immediate and
high= better delay (R²= .11; R²= .31, respectively). (b) The main effect of ADHD (quadrat-
ic) on the proportion of consistency in the three levels: low (solid line), medium (dotted
line), and high (dashed line)
To test the hypothesis of inconsistency related to a high level of symptoms of
ADHD, we performed a second repeated measures ANOVA with consistency scores of
the previous k levels (low, medium, high) as the dependent variable. The analysis yielded
a significant main effect for ASRS and the quadratic covariate ASRS squared (F (1, 83) =
13.75, p < 0.001, η
= 0.14, 95% CI (0.03, 0.28); F (1, 83) = 33.10, p < 0.001, η
= 0.29, 95%
CI (0.13, 0.42); respectively), indicating that higher ASRS levels were related to lower
consistency levels (Figure 2b). Interaction effects between both covariates and con-
sistency scores were not found (all p’s > 0.05).
The results show that participants with a high level of ADHD symptoms demon-
strated sub-optimal choices both when the better choice was the immediate one and
when it was the delayed one. In other words, the same person with a high level of ADHD
symptoms was more likely to choose both the immediate option when the delayed was
better and the delayed option when the immediate was better. Further, high ADHD was
related to less consistency in both contexts. The results support the claim that ADHD is
characterized by sub-optimal and inconsistent temporal decision-making rather than
steeper delay discounting and an immediacy bias.
4. Discussion
Previous studies have suggested that people with ADHD are characterized by a
higher immediacy bias than controls in their temporal decisions [6,7]. Yet these studies
did not differentiate between settings where the delayed option is better and those where
the immediate option is better. This study explored an alternative explanation for ADHD
temporal decisions, suggesting that people with ADHD might be characterized by
sub-optimal decisions and are less consistent, rather than being merely impatient. An
intriguing implication of this mechanism is that people with ADHD might show more
patience than controls in situations where they actually should choose the immediate
option.
Two experiments were conducted to confront these competing hypotheses. The first
compared a delay discounting paradigm where the delayed option was better with a
condition where the immediate option was better. The second experiment tested whether
the results of the first experiment were replicated at the individual level using a with-
in-subject design. The results support the hypothesis that a high level of ADHD symp-
toms is related to sub-optimality in the valuation of the delay, rather than steeper delay
discounting. This pattern implies that a person with ADHD might appear either impa-
tient when the delayed reward is better or too patient when the immediate is better.
Sub-optimal decision-making in people with ADHD was also evident in research on
decision-making under risk. Dekkers et al. [31] conducted a study where they included
risky items with both high and low expected values (i.e., sometimes it was better to
choose the risky gamble, and sometimes it was better to choose the safe option). They
found that people with ADHD chose the risky option more often than controls when it
was associated with a low expected value, but chose the risky option less often than
controls when it was associated with a high expected value. These findings support the
sub-optimal hypothesis rather than the risk-seeking or risk-taking bias hypothesis. Fur-
ther research is needed to evaluate the possibility that sub-optimal decision-making by
people with ADHD generalizes to other domains beyond risk and temporal decisions.
The second aim of this study was to explore inconsistency as a possible explanation
for sub-optimal decision-making in ADHD. How can inconsistency explain the
sub-optimal decisions of people with ADHD? For an example of a temporal discounting
Brain Sci. 2022, 12, 1312 8 of 13
decision, the choice of whether to wait is determined by the discounting rate assigned to
the delayed reward. For example, when facing a series of trials in which the immediate
option is better (i.e., the k value is relatively high), typically, the discounting rate of the
participant would be relatively consistent across trials, resulting in choosing the better
option for most trials. A participant with a high level of ADHD would have a similar
discounting rate on average. However, due to inconsistency, in some trials, the dis-
counting rate would be lower than that of the participant with low ADHD, resulting in
choosing the immediate option. Nevertheless, in other trials, the discounting rate of the
participant with high ADHD would be higher than that of the participant with low
ADHD, resulting in more choices of the delayed reward, which is the worse option in this
example. As such, this inconsistency can account for sub-optimal choices, regardless of
their direction.
It is worthwhile noting that the current study does not rule out the possibility that
ADHD is related to steeper delay discounting. It simply shows that steeper delay dis-
counting cannot account for all ADHD-related temporal decisions, and specifically in
situations where the immediate option is better. Therefore, steeper delay discounting is
not the only explanation for the temporal choices of people with ADHD, and the incon-
sistency that characterizes their decisions explains at least part of the decision-making
process in ADHD. Further, decision-making inconsistency in relation to ADHD was also
found in the context of risky decision-making in a subsequent analysis of the data from
Dekkers et al. [31]. Specifically, ADHD was associated with lower consistency in the
weight given to the risk [25]. Evidently, inconsistency in ADHD is a strong component of
both conditions presented here, and we suggest that it is the main driver behind the
sub-optimal decision-making of people with ADHD.
We tested both linear and quadratic models. In all comparisons, the quadratic model
was superior, suggesting that ADHD does not affect temporal decision-making monot-
onously as far as decision-making is concerned. Much like other cognitive, personality,
and psychiatric qualities, it is probably true that any extreme is not ideal [32]. This ten-
dency may open up new directions for research into other ADHD-related behaviors.
Every study has limitations, and this one is not exceptional. First, this study’s defi-
nition of high and low ADHD was based on the ASRS questionnaire that probes symp-
toms only. It is possible that recruiting a clinically diagnosed ADHD group could have
shed some light on the role of functional impairment in the link between ADHD and
sub-optimal decision-making, which was neglected in the current investigation. Further,
it is possible that the use of the ASRS, valid and reliable as it is, might not yield clinical
implications, because it may indicate a more general cognitive dysfunction that is not
necessarily ADHD. However, we based our choice on the support for a dimensional
perspective, rather than a categorical one, on ADHD [27].
Second, old age was not considered an exclusion criterion, and therefore the age
range was wide (18–74). Importantly, our findings could hence be attributed to the ad-
vanced age of some of our participants, and the related neurological diseases and deficits.
However, re-examining the data with age as a covariate did not have an impact on the
results. We further made sure that participants did not report taking any medication for
the treatment of neurological diseases. Future studies should take that issue into account
in advance.
Third, this study explored delay discounting using an experimental task. The use of
the present and similar tasks to examine delay discounting is accepted and well-spread
[6–8]. Yet, these tasks might be criticized for not representing real-world decisions that
may involve delay discounting. That said, the scope of the current study was limited to
the, mainly, theoretical understanding of the link between ADHD and temporal deci-
sion-making, and for that, an experimental task was sufficient.
Fourth, some authors recommended excluding responses with consistency scores
lower than 0.8 in the MCQ paradigm [33]. Here, we hypothesized that inconsistency
would be related to ADHD, and therefore, we did not implement that exclusion criterion.
Brain Sci. 2022, 12, 1312 9 of 13
However, we repeated our analysis with the exclusion criterion to test whether our
findings would emerge even with consistency scores only above 80%. The results show
that the effects of the condition where the delayed options were better remained signifi-
cant, but the weaker effects of the better–immediate condition did not reach significance.
An examination of the ADHD scores (ASRS mean) before and after the filtering revealed
that the original mean of 2.36 (SD = 0.73) had dropped to 2.16 (SD = 0.55). On the other
hand, the ADHD level of the 53 filtered participants was significantly higher (M = 2.83,
SD = 0.87; t(196) = −5.89, p < 0.001). Therefore, it seems that employing the exclusion cri-
terion removed many of our high-ADHD participants, preventing the replication of some
of the findings. Importantly, this demonstration suggests that consistency, or incon-
sistency, is a fundamental mechanism of ADHD. It also indicates that inconsistency ap-
pears together with steeper delay discounting when the delayed option is optimal.
However, when the immediate option is optimal, a high inconsistency that characterizes
the far end of the ADHD symptoms continuum may be the source of the effects we re-
ported in the results section above.
Lastly, the suggested mechanism of inconsistency is not the only plausible explana-
tion. In fact, it has been suggested that the sub-optimal decision-making of people with
ADHD can be caused by the use of less complex strategies [34]. Further research should
explore causality, confront the different explanations, or coherently unify them.
Further research should also investigate sub-optimality in the decision-making
process of people with ADHD from a developmental perspective [35], with clinical sam-
ples, and study the link in real-life delay situations. Future studies should also focus on
other domains of decision-making, such as social, financial, and health-related deci-
sion-making. They should also examine whether inconsistency in decision-making re-
lates to inconsistency in performance, e.g., variability in reaction time on cognitive tests.
Importantly, future research should further develop inconsistency as a theoretical ex-
planation of the phenomena, and examine the option to utilize that construct for clinical
practice.
5. Conclusions
In conclusion, the current study’s findings suggest that ADHD symptoms might not
be linked to steeper delay discounting, but rather to a sub-optimal valuation of the delay,
leading to either impatient or overly patient sub-optimal choices. Importantly, ADHD is
linked to general inconsistency, which can account for the sub-optimal decision-making
deficit demonstrated in this study. Further research should investigate sub-optimality in
the decision-making of people with ADHD, and inconsistency as a mechanism.
Author Contributions: Conceptualization, O.G.-S., E.E. and Y.P.; methodology, O.G.-S., E.E. and
Y.P.; validation, O.G.-S. and Y.P.; formal analysis, O.G.-S. and Y.P.; investigation, O.G.-S.; re-
sources, Y.P.; writing—original draft preparation, O.G.-S.; writing—review and editing, E.E. and
Y.P.; visualization, O.G.-S.; supervision, E.E. and Y.P.; project administration, O.G.-S. and Y.P.;
funding acquisition, Y.P. All authors have read and agreed to the published version of the manu-
script.
Funding: This research was funded by the Milton Rosenbaum fund (grant no. 181218).
Institutional Review Board Statement: The study was conducted in accordance with the Declara-
tion of Helsinki, and approved by the Ethics Committee of The Hebrew University of Jerusalem
(protocol code 2019C04 27 February 2019).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the
study.
Data Availability Statement: The data presented in this study are available upon request from the
corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
Brain Sci. 2022, 12, 1312 10 of 13
Appendix A
Table A1. Experiment 1 The monetary-choice questionnaire (MCQ) revised scale items with asso-
ciated discount parameter (k), order of presentation, and proportion of choices in the delayed op-
tion.
Condition “Delayed-is-Better”
Item Values
Order Immediate Reward Delayed Reward Delay (Days) k % Choices of Delayed Reward
12 18 35 21 0.045 81
1 29 55 20 0.045 75
8 41 80 21 0.045 82
18 12 30 24 0.061 72
5 23 50 19 0.061 81
15 43 85 16 0.061 85
23 10 25 20 0.075 74
21 24 60 20 0.075 82
11 30 75 20 0.075 84
19 12 30 18 0.082 85
25 25 60 17 0.082 88
14 38 85 15 0.082 81
3 10 25 15 0.1 75
9 22 55 15 0.1 86
2 30 75 15 0.1 83
16 14 35 10 0.15 82
26 20 50 10 0.15 84
7 32 80 10 0.15 90
4 10 25 6 0.25 85
13 20 50 6 0.25 87
20 25 75 8 0.25 87
6 14 35 5 0.3 91
22 24 60 5 0.3 88
17 32 80 5 0.3 86
10 11 30 5 0.35 88
24 20 55 5 0.35 89
27 50 85 2 0.35 89
Condition “Immediate-is-Better”
12 33 35 360 0.00016 9
1 53 55 220 0.00016 10
8 78 80 165 0.00016 15
18 29 30 170 0.0002 5
5 49 50 117 0.0002 11
15 82 85 180 0.0002 15
23 23 25 218 0.0004 9
21 56 60 177 0.0004 15
11 71 75 143 0.0004 16
19 27 30 155 0.0007 16
25 55 60 129 0.0007 18
14 78 85 128 0.0007 21
3 22 25 136 0.001 18
9 49 55 120 0.001 18
2 67 75 117 0.001 24
Brain Sci. 2022, 12, 1312 11 of 13
16 30 35 113 0.0015 21
26 43 50 108 0.0015 29
7 68 80 118 0.0015 39
4 20 25 140 0.0018 19
13 40 50 136 0.0018 32
20 62 75 116 0.0018 39
6 30 35 80 0.0021 22
22 51 60 84 0.0021 35
17 67 80 92 0.0021 40
10 25 30 80 0.0025 23
24 44 55 100 0.0025 35
27 68 85 100 0.0025 44
Table A2. Experiment 2 MCQ revised scale items with associated discount parameter (k), order of
presentation, and proportion of choices in the delayed option.
Item Values
Order Immediate Reward Delayed Reward Delay (Days) k % Choices of Delayed Reward
13 34 35 186 0.00016 12
1 54 55 92 0.0002 13
9 78 80 64 0.0004 18
20 28 30 100 0.0007 17
6 48 50 40 0.001 19
17 80 85 51 0.0012 24
26 22 25 90 0.0015 23
24 56 60 39 0.0018 27
12 61 75 109 0.0021 37
22 24 30 100 0.0025 27
16 46 60 51 0.006 57
15 66 85 41 0.007 59
3 17 25 59 0.008 45
10 34 55 69 0.009 51
2 35 75 71 0.016 71
18 16 35 59 0.02 69
21 23 50 47 0.025 75
25 39 80 35 0.03 82
5 12 25 30 0.035 81
14 22 50 31 0.041 23
23 17 30 17 0.045 82
7 24 55 21 0.061 83
8 40 85 15 0.075 87
19 10 25 18 0.082 86
11 10 50 40 0.1 81
27 30 75 10 0.15 86
4 10 35 10 0.25 86
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